This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.

Knit to html for best performance.

Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.

What needs to be done for PFAS assessment?

  1. Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE
  2. Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
  3. Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
  4. The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution.
  5. Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
  6. Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
  7. Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
  8. Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
  9. Add dioxin and MeHg concentrations. Later.
  10. Now that the model runs technically, look through each part to check that it makes sense.
  11. If PAF contains some inputs with Age and some without Age (implying that they apply to any age group), only the explicit age groups remain in the end. Add RRorig to compensate for this.
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]
## Loading objects:
##   RR
RRorig <- RR@formula

RR@formula <- function(...) {
  out <- RRorig()
  out <- out * Ovariable(output=data.frame(Age=c(
    "0 - 4", "5 - 9", "10 - 14", "15 - 19", "20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54",
    "55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85+"),
    Result=1),
    marginal=c(TRUE,FALSE)
  )
  return(out)
}
# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]

dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)

CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)

cat("Laskennassa käytetty data.\n")
## Laskennassa käytetty data.
dat
cat("Tarkastellut päätökset.\n")
## Tarkastellut päätökset.
dec
cat("Aggregoidut marginaalit.\n")
## Aggregoidut marginaalit.
CTable
dummy <- Ovariable("dummy",data=data.frame(Age="dummy", Result=1))

fish_proportion <- Ovariable( # How age groups eat fish differently
  "fish_proportion",
  dependencies = data.frame(Name="dummy"),
  formula = function(...) {
    out = prepare(dat,"fish_proportion",c("Type","Exposure_agent","Response","Unit"))
    out$Result <- as.numeric(as.character(out$Result))
    out$Result <- out$Result / sum(out$Result) * length(out$Result)
    return(out)
  },
  unit="-")

amount <- Ovariable(
  "amount",
  dependencies = data.frame(Name="fish_proportion"),
  formula = function(...) {
    amount = Ovariable("total_amount", data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")))
      # Filleted weight, i.e. no loss.
    amount <- amount * 1000 / 5.52 /365.25 
      # M kg/a per 5.52M population --> g/d per average person.
    amount <- amount * fish_proportion
      # fish_proportion tells the relative amount in each subgroup
  
    # Match KKE-classification in amount with Fineli classification
    tmp <- Ovariable(
      output = data.frame(
        Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
        Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
        Result = 1
      ),
      marginal = c(TRUE, TRUE, FALSE)
    )
    
    amount <- amount * tmp
    
    return(amount)
  },
  unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included

amount <- EvalOutput(amount)
## Loading required package: reshape2
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
ggplot(amount@output, aes(x=Age, weight=amountResult, fill=Kala))+geom_bar()+
  labs(
    title="Kalansyönti Suomessa ikäryhmittäin",
    y="Syönti (g/d)"
  )

ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")
## Saving 7 x 5 in image
population <- Ovariable(
  "population",
  data = prepare(dat,"population",c("Type","Exposure_agent","Response","Unit")),
  unit="#")

population <- Ovariable(
  "population", 
  data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
  unit = "#"
)

incidence <- Ovariable(
  "incidence",
  data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
  unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""

case_burden <- Ovariable(
  "case_burden",
  data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
  unit="DALY/case")

ERFchoice <- Ovariable(
  "ERFchoice",
  data = 
    prepare(dat, "ERFchoice", c("Unit", "Type"))
)

InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
  "InpBoD",
  data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
  unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"
conc_vit <- Ovariable(
  "conc_vit",
  ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
  subset = "Fineli data for common fish species"
)
  df = conc_vit@data
  df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
  df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
  df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
  df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
  df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
  df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
  df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df

######## Concentration of PFAS

# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
#       POP     mean       sd      min   Q0.025   median   Q0.975      max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935

# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
#      POP   mean       sd      min   Q0.025   median   Q0.975      max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994

conc_eukalat <- EvalOutput(Ovariable(
  "conc_eukalat",
  data = data.frame(
    Area = c("Suomi","Helsinki"),
    Compound="PFOS",
    Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
  unit="ng/g fresh weight"
))

conc_pfas <- Ovariable(
  "conc_pfas",
  data=opbase.data("Op_fi5932",subset="PFAS concentrations"),
  unit="ng/g fresh weight")
conc_pfas@data$Area <- "Porvoo"
conc_pfas <- EvalOutput(conc_pfas)

ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
  scale_x_log10()+
  stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
  scale_linetype_manual(values=c("dotted","solid","twodash"))+
  labs(
    title="PFAS concentration in fishes in Finland",
    x="PFAS concentration (ng/g fresh weight)",
    y="Cumulative probability"
  )

# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")
## Saving 7 x 5 in image
sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
tmp <- conc_pfas / sum_pfas
summary(tmp, marginals="Compound")
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of 
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.

conc <- Ovariable(
  "conc",
  dependencies = data.frame(Name="conc_vit", "conc_pfas"),
  formula = function(...){
    conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
    colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"

    conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
    conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
    conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
    
    out <- OpasnetUtils::combine(conc_vit, conc_pfas)
    return(out)
  }
)
conc <- EvalOutput(conc)

ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
  facet_wrap(~Compound, scales="free_x")

###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#

mc2dparam<- list(
  N2 = 10, # Number of iterations in the new Iter
  strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
  run2d = FALSE, # Should the mc2d function be used or not?
  info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
  newmarginals = c("Group","Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
  method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
  fun = mean # Function for aggregating the first Iter dimension.
)

if(FALSE) {
## Exposure with background exposure but without mother's exposure to child

expo_dir <- Ovariable(
  "expo_dir",
  dependencies=data.frame(Name=c("amount","conc","expo_bg")),
  formula = function(...) {
    out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
    out$Exposure_agent <- "Fish"
    out <- combine(conc, out, name="conc")
    out <- oapply(amount * out, cols="Fish", FUN=sum)
    out <- Ovariable(output = data.frame(
      Exposcen = c("BAU", "No exposure"),
      Result = c(1, 0)
    ), marginal=c(TRUE,FALSE)) * out + expo_bg
    out$Exposure <- as.factor(
      ifelse(
        out$Exposure_agent %in% c("DHA", "MeHg"),
        "To child",
        "To eater"
      )
    )
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)

## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
  "addexposure",
  ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
  subset = "Background exposure",
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
  "expo_bg",
  dependencies = data.frame(Name="addexposure","info"),
  formula = function(...) {
    out <- addexposure
    
    # Empty values ("") in indices must be replaced by NA so that Ops works correctly.
    levels(out$Gender)[levels(out$Gender) == ""] <- NA
    levels(out$Country)[levels(out$Country) == ""] <- NA
    levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
    out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
    
    temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
    temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
    temp1$Exposure_agent <- "TEQ"
    
    temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
    temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
    temp2$Exposure_agent <- "Omega3"
    
    out <- combine(out, temp1, temp2)
    out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
    
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Stores non-marginal columns for further use.
info <- Ovariable(
  "info",
  dependencies = data.frame(Name = c("jsp")),
  formula = function(...) {
    out <- jsp
    out$Group <- factor(
      paste(out$Gender, out$Ages),
      levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
    )
    out$Country <- factor(out$Country, ordered=FALSE)
    out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
    out$Result <- 1
    return(out)
  }
)

} # END IF
############################### Code from Goherr assessment ends

expo_dir <- Ovariable(
  "expo_dir",
  dependencies = data.frame(Name=c("conc", "amount")),
  formula = function(...) {
    
    conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
    conc <- oapply(conc, cols="", FUN=mean)
    out <- conc * amount 
    
#    colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
      
    return(out)
  },
  unit="ng/d"
)

expo_dir <- EvalOutput(expo_dir)
#View(expo_dir@output)

ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
       aes(x=Age, weight=expo_dirResult,fill=Fish))+geom_bar()+
  facet_wrap(~Compound, scales="free_y")+
  labs(title="Eri yhdisteiden saanti kalasta")

ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
## Saving 7 x 5 in image
exposure <- Ovariable(
  "exposure",
  dependencies = data.frame(
    Name = c(
      "expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
      "expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
      "mc2d" # 2D Monte Carlo function
    ),
    Ident = c(
      NA,
      "Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
      "Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
  ),
  formula = function(...) {
    out <- OpasnetUtils::combine(expo_dir, expo_indir)
    out <- unkeep(out, "Source.1", sources=TRUE)
    out <- mc2d(out)
    out$Exposure[is.na(out$Exposure)] <- "Direct"
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)

exposure <- Ovariable("exposure", data=data.frame(Result=1))
if(FALSE) {
exposure <- EvalOutput(exposure)
View(exposure@output)

ggplot(exposure@output, aes(x=Age, weight=exposureResult, fill=Fish))+geom_bar()+
  facet_grid(Compound~Exposure, scales="free_y")+
  labs(
    title="Exposure to compounds",
    y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
  )
} # END IF
objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]
## Loading objects:
##   BoDattr
tryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))
##  Evaluating BoDattr ...
## 
##  - BoD fetched successfully!
## 
##  - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
## 
##  done(0 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
## 
##  done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
## 
##  done(0.01 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
## 
## - done(0.23 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
##  done!
## - Evaluating PAF ...
## 
##  - - dose fetched successfully!
## 
##  - - ERF fetched successfully!
## 
##  - - frexposed fetched successfully!
## 
##  - - P_illness fetched successfully!
## 
##  - - sumExposcen fetched successfully!
## 
##  - - mc2d fetched successfully!
## - - Evaluating dose ...
## 
##  - - - BW fetched successfully!
## - - - Evaluating exposure ...
## 
##  done(0 secs)!
## - - - Checking exposure marginals ... exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ... done!
## - - - Evaluating BW ...
## 
##  done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
## 
## -- done(23.24 secs)!
## - - Checking dose marginals ... Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
## 
##  - - - ERF_env fetched successfully!
## 
##  - - - ERF_omega3 fetched successfully!
## 
##  - - - ERF_mehg fetched successfully!
## 
##  - - - ERF_diox fetched successfully!
## 
##  - - - ERF_vit fetched successfully!
## 
##  - - - ERF_micr fetched successfully!
## 
##  - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
## 
##  done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
## 
##  done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
## 
##  done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
## 
##  done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
## 
##  done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
## 
##  done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
## 
##  done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
## 
##  done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
## 
## -- done(2.84 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
## 
## -- done(0.13 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
## 
##  done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
## 
##  done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
## 
## - done(5.61 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
##  done!
## 
##  done(6.42 mins)!
##  Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, PAFSource, BoDattrSource recognized as marginal(s).
oprint(summary(amount,"mean"))
##               Kala Scenario              Age          Fish       mean
## 1      Kaupallinen      BAU     Female 18-45  Average fish  0.7187139
## 2       Muu tuonti      BAU     Female 18-45  Average fish  6.2887467
## 3       Vapaa-ajan      BAU     Female 18-45  Average fish  2.2235211
## 4      Kaupallinen      BAU       Female 45+  Average fish  1.7967848
## 5       Muu tuonti      BAU       Female 45+  Average fish 15.7218667
## 6       Vapaa-ajan      BAU       Female 45+  Average fish  5.5588029
## 7      Kaupallinen      BAU Non Female 18-45  Average fish  1.4374278
## 8       Muu tuonti      BAU Non Female 18-45  Average fish 12.5774934
## 9       Vapaa-ajan      BAU Non Female 18-45  Average fish  4.4470423
## 10     Kaupallinen      BAU   Non Female 45+  Average fish  2.3957130
## 11      Muu tuonti      BAU   Non Female 45+  Average fish 20.9624889
## 12      Vapaa-ajan      BAU   Non Female 45+  Average fish  7.4117372
## 13         Silakka      BAU     Female 18-45       Herring  0.3593570
## 14         Silakka      BAU       Female 45+       Herring  0.8983924
## 15         Silakka      BAU Non Female 18-45       Herring  0.7187139
## 16         Silakka      BAU   Non Female 45+       Herring  1.1978565
## 17       Kirjolohi      BAU     Female 18-45 Rainbow trout  1.5048072
## 18 Tuontikirjolohi      BAU     Female 18-45 Rainbow trout  1.0780709
## 19       Kirjolohi      BAU       Female 45+ Rainbow trout  3.7620181
## 20 Tuontikirjolohi      BAU       Female 45+ Rainbow trout  2.6951771
## 21       Kirjolohi      BAU Non Female 18-45 Rainbow trout  3.0096145
## 22 Tuontikirjolohi      BAU Non Female 18-45 Rainbow trout  2.1561417
## 23       Kirjolohi      BAU   Non Female 45+ Rainbow trout  5.0160241
## 24 Tuontikirjolohi      BAU   Non Female 45+ Rainbow trout  3.5935695
## 25      Tuontilohi      BAU     Female 18-45        Salmon  5.3005151
## 26      Tuontilohi      BAU       Female 45+        Salmon 13.2512876
## 27      Tuontilohi      BAU Non Female 18-45        Salmon 10.6010301
## 28      Tuontilohi      BAU   Non Female 45+        Salmon 17.6683835
## 29      Kasvatettu      BAU     Female 18-45     Whitefish  0.1347589
## 30      Kasvatettu      BAU       Female 45+     Whitefish  0.3368971
## 31      Kasvatettu      BAU Non Female 18-45     Whitefish  0.2695177
## 32      Kasvatettu      BAU   Non Female 45+     Whitefish  0.4491962
oprint(summary(BoD,"mean"))
##                                              Response       Age Gender Adjust
## 1                           Loss in child's IQ points     0 - 4 Female    BAU
## 2                                 Sperm concentration     0 - 4 Female    BAU
## 3                             Yes or no dental defect     0 - 4 Female    BAU
## 4                                 All-cause mortality     0 - 4 Female    BAU
## 5                                    Cancer morbidity     0 - 4 Female    BAU
## 6                                          Depression     0 - 4 Female    BAU
## 7                                      CHD2 mortality     0 - 4 Female    BAU
## 8                                   Immunosuppression     0 - 4 Female    BAU
## 9        Dioxin recommendation tolerable daily intake Undefined Female    BAU
## 10  Dioxin recommendation tolerable daily intake 2018 Undefined Female    BAU
## 11                                           PFAS TWI Undefined Female    BAU
## 12                           Vitamin D recommendation Undefined Female    BAU
## 13                                All-cause mortality     5 - 9 Female    BAU
## 14                                   Cancer morbidity     5 - 9 Female    BAU
## 15                                         Depression     5 - 9 Female    BAU
## 16                                     CHD2 mortality     5 - 9 Female    BAU
## 17                                  Immunosuppression     5 - 9 Female    BAU
## 18                                All-cause mortality   10 - 14 Female    BAU
## 19                                   Cancer morbidity   10 - 14 Female    BAU
## 20                                         Depression   10 - 14 Female    BAU
## 21                                     CHD2 mortality   10 - 14 Female    BAU
## 22                                All-cause mortality   15 - 19 Female    BAU
## 23                                   Cancer morbidity   15 - 19 Female    BAU
## 24                                         Depression   15 - 19 Female    BAU
## 25                                     CHD2 mortality   15 - 19 Female    BAU
## 26                                      Breast cancer   15 - 19 Female    BAU
## 27                                All-cause mortality   20 - 24 Female    BAU
## 28                                   Cancer morbidity   20 - 24 Female    BAU
## 29                                         Depression   20 - 24 Female    BAU
## 30                                     CHD2 mortality   20 - 24 Female    BAU
## 31                                      Breast cancer   20 - 24 Female    BAU
## 32                                All-cause mortality   25 - 29 Female    BAU
## 33                                   Cancer morbidity   25 - 29 Female    BAU
## 34                                         Depression   25 - 29 Female    BAU
## 35                                     CHD2 mortality   25 - 29 Female    BAU
## 36                                      Breast cancer   25 - 29 Female    BAU
## 37                                All-cause mortality   30 - 34 Female    BAU
## 38                                   Cancer morbidity   30 - 34 Female    BAU
## 39                                         Depression   30 - 34 Female    BAU
## 40                                     CHD2 mortality   30 - 34 Female    BAU
## 41                                      Breast cancer   30 - 34 Female    BAU
## 42                                All-cause mortality   35 - 39 Female    BAU
## 43                                   Cancer morbidity   35 - 39 Female    BAU
## 44                                         Depression   35 - 39 Female    BAU
## 45                                     CHD2 mortality   35 - 39 Female    BAU
## 46                                      Breast cancer   35 - 39 Female    BAU
## 47                                All-cause mortality   40 - 44 Female    BAU
## 48                                   Cancer morbidity   40 - 44 Female    BAU
## 49                                         Depression   40 - 44 Female    BAU
## 50                                     CHD2 mortality   40 - 44 Female    BAU
## 51                                      Breast cancer   40 - 44 Female    BAU
## 52                                All-cause mortality   45 - 49 Female    BAU
## 53                                   Cancer morbidity   45 - 49 Female    BAU
## 54                                         Depression   45 - 49 Female    BAU
## 55                                     CHD2 mortality   45 - 49 Female    BAU
## 56                                      Breast cancer   45 - 49 Female    BAU
## 57                                All-cause mortality   50 - 54 Female    BAU
## 58                                   Cancer morbidity   50 - 54 Female    BAU
## 59                                         Depression   50 - 54 Female    BAU
## 60                                     CHD2 mortality   50 - 54 Female    BAU
## 61                                      Breast cancer   50 - 54 Female    BAU
## 62                                All-cause mortality   55 - 59 Female    BAU
## 63                                   Cancer morbidity   55 - 59 Female    BAU
## 64                                         Depression   55 - 59 Female    BAU
## 65                                     CHD2 mortality   55 - 59 Female    BAU
## 66                                      Breast cancer   55 - 59 Female    BAU
## 67                                All-cause mortality   60 - 64 Female    BAU
## 68                                   Cancer morbidity   60 - 64 Female    BAU
## 69                                         Depression   60 - 64 Female    BAU
## 70                                     CHD2 mortality   60 - 64 Female    BAU
## 71                                      Breast cancer   60 - 64 Female    BAU
## 72                                All-cause mortality   65 - 69 Female    BAU
## 73                                   Cancer morbidity   65 - 69 Female    BAU
## 74                                         Depression   65 - 69 Female    BAU
## 75                                     CHD2 mortality   65 - 69 Female    BAU
## 76                                      Breast cancer   65 - 69 Female    BAU
## 77                                All-cause mortality   70 - 74 Female    BAU
## 78                                   Cancer morbidity   70 - 74 Female    BAU
## 79                                         Depression   70 - 74 Female    BAU
## 80                                     CHD2 mortality   70 - 74 Female    BAU
## 81                                      Breast cancer   70 - 74 Female    BAU
## 82                                All-cause mortality   75 - 79 Female    BAU
## 83                                   Cancer morbidity   75 - 79 Female    BAU
## 84                                         Depression   75 - 79 Female    BAU
## 85                                     CHD2 mortality   75 - 79 Female    BAU
## 86                                      Breast cancer   75 - 79 Female    BAU
## 87                                All-cause mortality   80 - 84 Female    BAU
## 88                                   Cancer morbidity   80 - 84 Female    BAU
## 89                                         Depression   80 - 84 Female    BAU
## 90                                     CHD2 mortality   80 - 84 Female    BAU
## 91                                      Breast cancer   80 - 84 Female    BAU
## 92                                All-cause mortality   85 - 89 Female    BAU
## 93                                   Cancer morbidity   85 - 89 Female    BAU
## 94                                         Depression   85 - 89 Female    BAU
## 95                                     CHD2 mortality   85 - 89 Female    BAU
## 96                                      Breast cancer   85 - 89 Female    BAU
## 97                                All-cause mortality   90 - 94 Female    BAU
## 98                                   Cancer morbidity   90 - 94 Female    BAU
## 99                                         Depression   90 - 94 Female    BAU
## 100                                    CHD2 mortality   90 - 94 Female    BAU
## 101                                     Breast cancer   90 - 94 Female    BAU
## 102                         Loss in child's IQ points     0 - 4   Male    BAU
## 103                               Sperm concentration     0 - 4   Male    BAU
## 104                           Yes or no dental defect     0 - 4   Male    BAU
## 105                               All-cause mortality     0 - 4   Male    BAU
## 106                                  Cancer morbidity     0 - 4   Male    BAU
## 107                                        Depression     0 - 4   Male    BAU
## 108                                    CHD2 mortality     0 - 4   Male    BAU
## 109                                 Immunosuppression     0 - 4   Male    BAU
## 110      Dioxin recommendation tolerable daily intake Undefined   Male    BAU
## 111 Dioxin recommendation tolerable daily intake 2018 Undefined   Male    BAU
## 112                                          PFAS TWI Undefined   Male    BAU
## 113                          Vitamin D recommendation Undefined   Male    BAU
## 114                               All-cause mortality     5 - 9   Male    BAU
## 115                                  Cancer morbidity     5 - 9   Male    BAU
## 116                                        Depression     5 - 9   Male    BAU
## 117                                    CHD2 mortality     5 - 9   Male    BAU
## 118                                 Immunosuppression     5 - 9   Male    BAU
## 119                               All-cause mortality   10 - 14   Male    BAU
## 120                                  Cancer morbidity   10 - 14   Male    BAU
## 121                                        Depression   10 - 14   Male    BAU
## 122                                    CHD2 mortality   10 - 14   Male    BAU
## 123                               All-cause mortality   15 - 19   Male    BAU
## 124                                  Cancer morbidity   15 - 19   Male    BAU
## 125                                        Depression   15 - 19   Male    BAU
## 126                                    CHD2 mortality   15 - 19   Male    BAU
## 127                                     Breast cancer   15 - 19   Male    BAU
## 128                               All-cause mortality   20 - 24   Male    BAU
## 129                                  Cancer morbidity   20 - 24   Male    BAU
## 130                                        Depression   20 - 24   Male    BAU
## 131                                    CHD2 mortality   20 - 24   Male    BAU
## 132                                     Breast cancer   20 - 24   Male    BAU
## 133                               All-cause mortality   25 - 29   Male    BAU
## 134                                  Cancer morbidity   25 - 29   Male    BAU
## 135                                        Depression   25 - 29   Male    BAU
## 136                                    CHD2 mortality   25 - 29   Male    BAU
## 137                                     Breast cancer   25 - 29   Male    BAU
## 138                               All-cause mortality   30 - 34   Male    BAU
## 139                                  Cancer morbidity   30 - 34   Male    BAU
## 140                                        Depression   30 - 34   Male    BAU
## 141                                    CHD2 mortality   30 - 34   Male    BAU
## 142                                     Breast cancer   30 - 34   Male    BAU
## 143                               All-cause mortality   35 - 39   Male    BAU
## 144                                  Cancer morbidity   35 - 39   Male    BAU
## 145                                        Depression   35 - 39   Male    BAU
## 146                                    CHD2 mortality   35 - 39   Male    BAU
## 147                                     Breast cancer   35 - 39   Male    BAU
## 148                               All-cause mortality   40 - 44   Male    BAU
## 149                                  Cancer morbidity   40 - 44   Male    BAU
## 150                                        Depression   40 - 44   Male    BAU
## 151                                    CHD2 mortality   40 - 44   Male    BAU
## 152                                     Breast cancer   40 - 44   Male    BAU
## 153                               All-cause mortality   45 - 49   Male    BAU
## 154                                  Cancer morbidity   45 - 49   Male    BAU
## 155                                        Depression   45 - 49   Male    BAU
## 156                                    CHD2 mortality   45 - 49   Male    BAU
## 157                                     Breast cancer   45 - 49   Male    BAU
## 158                               All-cause mortality   50 - 54   Male    BAU
## 159                                  Cancer morbidity   50 - 54   Male    BAU
## 160                                        Depression   50 - 54   Male    BAU
## 161                                    CHD2 mortality   50 - 54   Male    BAU
## 162                                     Breast cancer   50 - 54   Male    BAU
## 163                               All-cause mortality   55 - 59   Male    BAU
## 164                                  Cancer morbidity   55 - 59   Male    BAU
## 165                                        Depression   55 - 59   Male    BAU
## 166                                    CHD2 mortality   55 - 59   Male    BAU
## 167                                     Breast cancer   55 - 59   Male    BAU
## 168                               All-cause mortality   60 - 64   Male    BAU
## 169                                  Cancer morbidity   60 - 64   Male    BAU
## 170                                        Depression   60 - 64   Male    BAU
## 171                                    CHD2 mortality   60 - 64   Male    BAU
## 172                                     Breast cancer   60 - 64   Male    BAU
## 173                               All-cause mortality   65 - 69   Male    BAU
## 174                                  Cancer morbidity   65 - 69   Male    BAU
## 175                                        Depression   65 - 69   Male    BAU
## 176                                    CHD2 mortality   65 - 69   Male    BAU
## 177                                     Breast cancer   65 - 69   Male    BAU
## 178                               All-cause mortality   70 - 74   Male    BAU
## 179                                  Cancer morbidity   70 - 74   Male    BAU
## 180                                        Depression   70 - 74   Male    BAU
## 181                                    CHD2 mortality   70 - 74   Male    BAU
## 182                                     Breast cancer   70 - 74   Male    BAU
## 183                               All-cause mortality   75 - 79   Male    BAU
## 184                                  Cancer morbidity   75 - 79   Male    BAU
## 185                                        Depression   75 - 79   Male    BAU
## 186                                    CHD2 mortality   75 - 79   Male    BAU
## 187                                     Breast cancer   75 - 79   Male    BAU
## 188                               All-cause mortality   80 - 84   Male    BAU
## 189                                  Cancer morbidity   80 - 84   Male    BAU
## 190                                        Depression   80 - 84   Male    BAU
## 191                                    CHD2 mortality   80 - 84   Male    BAU
## 192                                     Breast cancer   80 - 84   Male    BAU
## 193                               All-cause mortality   85 - 89   Male    BAU
## 194                                  Cancer morbidity   85 - 89   Male    BAU
## 195                                        Depression   85 - 89   Male    BAU
## 196                                    CHD2 mortality   85 - 89   Male    BAU
## 197                                     Breast cancer   85 - 89   Male    BAU
## 198                               All-cause mortality   90 - 94   Male    BAU
## 199                                  Cancer morbidity   90 - 94   Male    BAU
## 200                                        Depression   90 - 94   Male    BAU
## 201                                    CHD2 mortality   90 - 94   Male    BAU
## 202                                     Breast cancer   90 - 94   Male    BAU
##           mean
## 1   16395.2448
## 2    4376.4000
## 3     336.1075
## 4    4367.2700
## 5     279.4400
## 6       0.4400
## 7      51.4100
## 8     272.0400
## 9     309.2078
## 10    899.5137
## 11   2810.9804
## 12    618.4157
## 13    909.3800
## 14    352.6300
## 15     87.5700
## 16     57.2800
## 17    248.0400
## 18   1012.2200
## 19    357.1200
## 20    680.2900
## 21     87.0900
## 22   1945.6100
## 23    386.2000
## 24   1657.4000
## 25    153.9800
## 26      3.0000
## 27   2684.3600
## 28    524.2800
## 29   2295.7100
## 30    228.8100
## 31     14.4000
## 32   2980.0200
## 33    728.7600
## 34   2185.6700
## 35    342.4600
## 36     72.4600
## 37   3569.3800
## 38   1173.6800
## 39   1876.2400
## 40    456.4700
## 41    289.2600
## 42   4372.6700
## 43   1750.8000
## 44   2009.3100
## 45    655.6500
## 46    525.1700
## 47   5831.2700
## 48   2660.2800
## 49   1980.0500
## 50   1017.7100
## 51    863.5600
## 52   8481.7500
## 53   4204.3800
## 54   1805.0000
## 55   1511.8900
## 56   1403.4500
## 57  13751.6900
## 58   7197.9400
## 59   2050.3500
## 60   2418.4000
## 61   2079.3300
## 62  19024.8900
## 63  10372.2500
## 64   2139.7100
## 65   3830.7900
## 66   2667.9900
## 67  25603.4700
## 68  14317.8700
## 69   2050.2600
## 70   6174.0400
## 71   3008.1400
## 72  34094.4500
## 73  18551.3000
## 74   1910.2400
## 75  10009.4600
## 76   3280.9700
## 77  44484.9600
## 78  21707.2100
## 79   1738.8100
## 80  16202.4200
## 81   3328.9100
## 82  41490.0000
## 83  15942.7400
## 84    992.1300
## 85  18996.6800
## 86   2448.5100
## 87  51345.4900
## 88  13735.8800
## 89    754.1900
## 90  28299.7900
## 91   1889.1800
## 92  49589.0500
## 93   8476.7900
## 94    508.9600
## 95  30471.0100
## 96   1151.1600
## 97  36317.0200
## 98   4083.6700
## 99    282.5900
## 100 23285.7800
## 101   589.9300
## 102 17161.5101
## 103  4580.9400
## 104   351.8162
## 105  5367.6600
## 106   339.6000
## 107     0.3900
## 108    55.8100
## 109   319.9300
## 110   301.6056
## 111   877.3982
## 112  2741.8694
## 113   603.2113
## 114   952.8700
## 115   338.4100
## 116    65.4000
## 117    40.2800
## 118   275.0300
## 119  1158.8700
## 120   337.0300
## 121   431.4800
## 122    63.5400
## 123  3986.2800
## 124   443.7500
## 125   984.2500
## 126   136.7100
## 127     1.1800
## 128  7865.3300
## 129   614.7700
## 130  1386.3200
## 131   279.3900
## 132     1.1700
## 133  9547.0600
## 134   862.1500
## 135  1417.2600
## 136   532.8300
## 137     1.1800
## 138  9851.2400
## 139  1122.2900
## 140  1303.8000
## 141   816.3700
## 142     1.0600
## 143 12178.6200
## 144  1607.4500
## 145  1434.1400
## 146  1680.8700
## 147     2.0300
## 148 14632.7600
## 149  2244.8500
## 150  1420.5100
## 151  2937.4200
## 152     3.1400
## 153 18900.8100
## 154  3494.3800
## 155  1263.3400
## 156  4722.7300
## 157     6.4600
## 158 28892.0400
## 159  6777.8600
## 160  1387.5600
## 161  8534.1800
## 162    11.3200
## 163 40698.9300
## 164 12102.4100
## 165  1394.6200
## 166 13664.1400
## 167    15.6400
## 168 52798.2800
## 169 18345.9500
## 170  1303.2200
## 171 19981.9000
## 172    18.2900
## 173 64496.5600
## 174 25248.0000
## 175  1212.9100
## 176 26192.0400
## 177    17.6900
## 178 75114.4400
## 179 29396.5100
## 180  1113.6600
## 181 32896.1500
## 182    27.1600
## 183 57367.0300
## 184 20087.7200
## 185   607.2700
## 186 27830.2900
## 187    15.5100
## 188 52592.3400
## 189 15565.9500
## 190   408.4000
## 191 27498.8800
## 192    12.5800
## 193 35941.6400
## 194  8349.3200
## 195   217.1900
## 196 20009.0600
## 197     5.2200
## 198 17196.2600
## 199  2990.6800
## 200    89.6400
## 201  9975.4500
## 202     2.4300
oprint(summary(BoDattr,"mean"))
##                                              Response       Age Gender Adjust
## 1                           Loss in child's IQ points     0 - 4 Female    BAU
## 2                           Loss in child's IQ points     0 - 4   Male    BAU
## 3                           Loss in child's IQ points     0 - 4 Female    BAU
## 4                           Loss in child's IQ points     0 - 4   Male    BAU
## 5                                 Sperm concentration     0 - 4 Female    BAU
## 6                             Yes or no dental defect     0 - 4 Female    BAU
## 7        Dioxin recommendation tolerable daily intake Undefined Female    BAU
## 8   Dioxin recommendation tolerable daily intake 2018 Undefined Female    BAU
## 9                                 Sperm concentration     0 - 4   Male    BAU
## 10                            Yes or no dental defect     0 - 4   Male    BAU
## 11       Dioxin recommendation tolerable daily intake Undefined   Male    BAU
## 12  Dioxin recommendation tolerable daily intake 2018 Undefined   Male    BAU
## 13                                           PFAS TWI Undefined Female    BAU
## 14                                           PFAS TWI Undefined   Male    BAU
## 15                           Vitamin D recommendation Undefined Female    BAU
## 16                           Vitamin D recommendation Undefined   Male    BAU
## 17                                All-cause mortality     0 - 4 Female    BAU
## 18                                         Depression     0 - 4 Female    BAU
## 19                                All-cause mortality     5 - 9 Female    BAU
## 20                                         Depression     5 - 9 Female    BAU
## 21                                All-cause mortality   10 - 14 Female    BAU
## 22                                         Depression   10 - 14 Female    BAU
## 23                                All-cause mortality   15 - 19 Female    BAU
## 24                                         Depression   15 - 19 Female    BAU
## 25                                All-cause mortality   20 - 24 Female    BAU
## 26                                         Depression   20 - 24 Female    BAU
## 27                                All-cause mortality   25 - 29 Female    BAU
## 28                                         Depression   25 - 29 Female    BAU
## 29                                All-cause mortality   30 - 34 Female    BAU
## 30                                         Depression   30 - 34 Female    BAU
## 31                                All-cause mortality   35 - 39 Female    BAU
## 32                                         Depression   35 - 39 Female    BAU
## 33                                All-cause mortality   40 - 44 Female    BAU
## 34                                         Depression   40 - 44 Female    BAU
## 35                                All-cause mortality   45 - 49 Female    BAU
## 36                                         Depression   45 - 49 Female    BAU
## 37                                All-cause mortality   50 - 54 Female    BAU
## 38                                         Depression   50 - 54 Female    BAU
## 39                                All-cause mortality   55 - 59 Female    BAU
## 40                                         Depression   55 - 59 Female    BAU
## 41                                All-cause mortality   60 - 64 Female    BAU
## 42                                         Depression   60 - 64 Female    BAU
## 43                                All-cause mortality   65 - 69 Female    BAU
## 44                                         Depression   65 - 69 Female    BAU
## 45                                All-cause mortality   70 - 74 Female    BAU
## 46                                         Depression   70 - 74 Female    BAU
## 47                                All-cause mortality   75 - 79 Female    BAU
## 48                                         Depression   75 - 79 Female    BAU
## 49                                All-cause mortality   80 - 84 Female    BAU
## 50                                         Depression   80 - 84 Female    BAU
## 51                                All-cause mortality     0 - 4   Male    BAU
## 52                                         Depression     0 - 4   Male    BAU
## 53                                All-cause mortality     5 - 9   Male    BAU
## 54                                         Depression     5 - 9   Male    BAU
## 55                                All-cause mortality   10 - 14   Male    BAU
## 56                                         Depression   10 - 14   Male    BAU
## 57                                All-cause mortality   15 - 19   Male    BAU
## 58                                         Depression   15 - 19   Male    BAU
## 59                                All-cause mortality   20 - 24   Male    BAU
## 60                                         Depression   20 - 24   Male    BAU
## 61                                All-cause mortality   25 - 29   Male    BAU
## 62                                         Depression   25 - 29   Male    BAU
## 63                                All-cause mortality   30 - 34   Male    BAU
## 64                                         Depression   30 - 34   Male    BAU
## 65                                All-cause mortality   35 - 39   Male    BAU
## 66                                         Depression   35 - 39   Male    BAU
## 67                                All-cause mortality   40 - 44   Male    BAU
## 68                                         Depression   40 - 44   Male    BAU
## 69                                All-cause mortality   45 - 49   Male    BAU
## 70                                         Depression   45 - 49   Male    BAU
## 71                                All-cause mortality   50 - 54   Male    BAU
## 72                                         Depression   50 - 54   Male    BAU
## 73                                All-cause mortality   55 - 59   Male    BAU
## 74                                         Depression   55 - 59   Male    BAU
## 75                                All-cause mortality   60 - 64   Male    BAU
## 76                                         Depression   60 - 64   Male    BAU
## 77                                All-cause mortality   65 - 69   Male    BAU
## 78                                         Depression   65 - 69   Male    BAU
## 79                                All-cause mortality   70 - 74   Male    BAU
## 80                                         Depression   70 - 74   Male    BAU
## 81                                All-cause mortality   75 - 79   Male    BAU
## 82                                         Depression   75 - 79   Male    BAU
## 83                                All-cause mortality   80 - 84   Male    BAU
## 84                                         Depression   80 - 84   Male    BAU
## 85                                     CHD2 mortality     0 - 4 Female    BAU
## 86                                     CHD2 mortality     5 - 9 Female    BAU
## 87                                     CHD2 mortality   10 - 14 Female    BAU
## 88                                     CHD2 mortality   15 - 19 Female    BAU
## 89                                      Breast cancer   15 - 19 Female    BAU
## 90                                     CHD2 mortality   20 - 24 Female    BAU
## 91                                      Breast cancer   20 - 24 Female    BAU
## 92                                     CHD2 mortality   25 - 29 Female    BAU
## 93                                      Breast cancer   25 - 29 Female    BAU
## 94                                     CHD2 mortality   30 - 34 Female    BAU
## 95                                      Breast cancer   30 - 34 Female    BAU
## 96                                     CHD2 mortality   35 - 39 Female    BAU
## 97                                      Breast cancer   35 - 39 Female    BAU
## 98                                     CHD2 mortality   40 - 44 Female    BAU
## 99                                      Breast cancer   40 - 44 Female    BAU
## 100                                    CHD2 mortality   45 - 49 Female    BAU
## 101                                     Breast cancer   45 - 49 Female    BAU
## 102                                    CHD2 mortality   50 - 54 Female    BAU
## 103                                     Breast cancer   50 - 54 Female    BAU
## 104                                    CHD2 mortality   55 - 59 Female    BAU
## 105                                     Breast cancer   55 - 59 Female    BAU
## 106                                    CHD2 mortality   60 - 64 Female    BAU
## 107                                     Breast cancer   60 - 64 Female    BAU
## 108                                    CHD2 mortality   65 - 69 Female    BAU
## 109                                     Breast cancer   65 - 69 Female    BAU
## 110                                    CHD2 mortality   70 - 74 Female    BAU
## 111                                     Breast cancer   70 - 74 Female    BAU
## 112                                    CHD2 mortality   75 - 79 Female    BAU
## 113                                     Breast cancer   75 - 79 Female    BAU
## 114                                    CHD2 mortality   80 - 84 Female    BAU
## 115                                     Breast cancer   80 - 84 Female    BAU
## 116                                    CHD2 mortality     0 - 4   Male    BAU
## 117                                    CHD2 mortality     5 - 9   Male    BAU
## 118                                    CHD2 mortality   10 - 14   Male    BAU
## 119                                    CHD2 mortality   15 - 19   Male    BAU
## 120                                     Breast cancer   15 - 19   Male    BAU
## 121                                    CHD2 mortality   20 - 24   Male    BAU
## 122                                     Breast cancer   20 - 24   Male    BAU
## 123                                    CHD2 mortality   25 - 29   Male    BAU
## 124                                     Breast cancer   25 - 29   Male    BAU
## 125                                    CHD2 mortality   30 - 34   Male    BAU
## 126                                     Breast cancer   30 - 34   Male    BAU
## 127                                    CHD2 mortality   35 - 39   Male    BAU
## 128                                     Breast cancer   35 - 39   Male    BAU
## 129                                    CHD2 mortality   40 - 44   Male    BAU
## 130                                     Breast cancer   40 - 44   Male    BAU
## 131                                    CHD2 mortality   45 - 49   Male    BAU
## 132                                     Breast cancer   45 - 49   Male    BAU
## 133                                    CHD2 mortality   50 - 54   Male    BAU
## 134                                     Breast cancer   50 - 54   Male    BAU
## 135                                    CHD2 mortality   55 - 59   Male    BAU
## 136                                     Breast cancer   55 - 59   Male    BAU
## 137                                    CHD2 mortality   60 - 64   Male    BAU
## 138                                     Breast cancer   60 - 64   Male    BAU
## 139                                    CHD2 mortality   65 - 69   Male    BAU
## 140                                     Breast cancer   65 - 69   Male    BAU
## 141                                    CHD2 mortality   70 - 74   Male    BAU
## 142                                     Breast cancer   70 - 74   Male    BAU
## 143                                    CHD2 mortality   75 - 79   Male    BAU
## 144                                     Breast cancer   75 - 79   Male    BAU
## 145                                    CHD2 mortality   80 - 84   Male    BAU
## 146                                     Breast cancer   80 - 84   Male    BAU
##     Exposure_agent          mean
## 1              DHA -1.788072e+01
## 2              DHA -1.871641e+01
## 3             MeHg  1.925616e+03
## 4             MeHg  2.015614e+03
## 5              TEQ  1.875600e+01
## 6              TEQ  1.043562e+01
## 7              TEQ -2.501773e+03
## 8              TEQ -1.911467e+03
## 9              TEQ  1.963260e+01
## 10             TEQ  1.092335e+01
## 11             TEQ -2.440264e+03
## 12             TEQ -1.864471e+03
## 13            PFAS  0.000000e+00
## 14            PFAS  0.000000e+00
## 15       Vitamin D  6.184157e+02
## 16       Vitamin D  6.032113e+02
## 17            Fish -9.294861e+00
## 18            Fish -2.336224e-03
## 19            Fish -1.935433e+00
## 20            Fish -4.649617e-01
## 21            Fish -2.154308e+00
## 22            Fish -3.612068e+00
## 23            Fish -4.140842e+00
## 24            Fish -8.800131e+00
## 25            Fish -5.713123e+00
## 26            Fish -1.218930e+01
## 27            Fish -6.342377e+00
## 28            Fish -1.160503e+01
## 29            Fish -7.596711e+00
## 30            Fish -9.962084e+00
## 31            Fish -9.306354e+00
## 32            Fish -1.066863e+01
## 33            Fish -1.241069e+01
## 34            Fish -1.051327e+01
## 35            Fish -1.805171e+01
## 36            Fish -9.583828e+00
## 37            Fish -2.926772e+01
## 38            Fish -1.088654e+01
## 39            Fish -4.049067e+01
## 40            Fish -1.136100e+01
## 41            Fish -5.449187e+01
## 42            Fish -1.088606e+01
## 43            Fish -7.256322e+01
## 44            Fish -1.014261e+01
## 45            Fish -9.467734e+01
## 46            Fish -9.232386e+00
## 47            Fish -8.830317e+01
## 48            Fish -5.267813e+00
## 49            Fish -1.092786e+02
## 50            Fish -4.004447e+00
## 51            Fish -1.142399e+01
## 52            Fish -2.070744e-03
## 53            Fish -2.027993e+00
## 54            Fish -3.472478e-01
## 55            Fish -2.466423e+00
## 56            Fish -2.290986e+00
## 57            Fish -8.484000e+00
## 58            Fish -5.225974e+00
## 59            Fish -1.673978e+01
## 60            Fish -7.360805e+00
## 61            Fish -2.031901e+01
## 62            Fish -7.525084e+00
## 63            Fish -2.096639e+01
## 64            Fish -6.922656e+00
## 65            Fish -2.591976e+01
## 66            Fish -7.614710e+00
## 67            Fish -3.114290e+01
## 68            Fish -7.542340e+00
## 69            Fish -4.022659e+01
## 70            Fish -6.707830e+00
## 71            Fish -6.149093e+01
## 72            Fish -7.367389e+00
## 73            Fish -8.661953e+01
## 74            Fish -7.404874e+00
## 75            Fish -1.123706e+02
## 76            Fish -6.919577e+00
## 77            Fish -1.372680e+02
## 78            Fish -6.440067e+00
## 79            Fish -1.598661e+02
## 80            Fish -5.913089e+00
## 81            Fish -1.220942e+02
## 82            Fish -3.224361e+00
## 83            Fish -1.119323e+02
## 84            Fish -2.168441e+00
## 85          Omega3 -1.820771e-01
## 86          Omega3 -2.028667e-01
## 87          Omega3 -3.084437e-01
## 88          Omega3 -5.453458e-01
## 89          Omega3 -1.538400e-03
## 90          Omega3 -8.103687e-01
## 91          Omega3 -7.384320e-03
## 92          Omega3 -1.212879e+00
## 93          Omega3 -3.715749e-02
## 94          Omega3 -1.616665e+00
## 95          Omega3 -1.483325e-01
## 96          Omega3 -2.322094e+00
## 97          Omega3 -2.693072e-01
## 98          Omega3 -3.604390e+00
## 99          Omega3 -4.428336e-01
## 100         Omega3 -5.354610e+00
## 101         Omega3 -7.196892e-01
## 102         Omega3 -8.565167e+00
## 103         Omega3 -1.066280e+00
## 104         Omega3 -1.356738e+01
## 105         Omega3 -1.368145e+00
## 106         Omega3 -2.186639e+01
## 107         Omega3 -1.542574e+00
## 108         Omega3 -3.545017e+01
## 109         Omega3 -1.682481e+00
## 110         Omega3 -5.738357e+01
## 111         Omega3 -1.707065e+00
## 112         Omega3 -6.727991e+01
## 113         Omega3 -1.255596e+00
## 114         Omega3 -1.002284e+02
## 115         Omega3 -9.687715e-01
## 116         Omega3 -1.976604e-01
## 117         Omega3 -1.426583e-01
## 118         Omega3 -2.250375e-01
## 119         Omega3 -4.841812e-01
## 120         Omega3 -6.051040e-04
## 121         Omega3 -9.895062e-01
## 122         Omega3 -5.999760e-04
## 123         Omega3 -1.887106e+00
## 124         Omega3 -6.051040e-04
## 125         Omega3 -2.891310e+00
## 126         Omega3 -5.435680e-04
## 127         Omega3 -5.953081e+00
## 128         Omega3 -1.040984e-03
## 129         Omega3 -1.040336e+01
## 130         Omega3 -1.610192e-03
## 131         Omega3 -1.672634e+01
## 132         Omega3 -3.312688e-03
## 133         Omega3 -3.022522e+01
## 134         Omega3 -5.804896e-03
## 135         Omega3 -4.839383e+01
## 136         Omega3 -8.020192e-03
## 137         Omega3 -7.076923e+01
## 138         Omega3 -9.379112e-03
## 139         Omega3 -9.276347e+01
## 140         Omega3 -9.071432e-03
## 141         Omega3 -1.165072e+02
## 142         Omega3 -1.392765e-02
## 143         Omega3 -9.856561e+01
## 144         Omega3 -7.953528e-03
## 145         Omega3 -9.739187e+01
## 146         Omega3 -6.451024e-03
oprint(summary(case_burden,"mean"))
##                                            Response        mean
## 1      Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3                         Loss in child's IQ points 0.110000000
## 4                                          PFAS TWI 0.001004988
## 5                               Sperm concentration 2.500000000
## 6                          Vitamin D recommendation 0.001004988
## 7                           Yes or no dental defect 0.060000000
oprint(summary(conc,"mean"))
##             Fish  Compound      mean
## 1   Average fish       ALA  0.690000
## 2          Bream       ALA  0.220000
## 3        Herring       ALA  1.740000
## 4           Pike       ALA  0.080000
## 5  Rainbow trout       ALA  4.810000
## 6          Roach       ALA  0.100000
## 7         Salmon       ALA  7.960000
## 8        Vendace       ALA  1.350000
## 9      Whitefish       ALA  2.220000
## 10  Average fish       DHA  2.540000
## 11         Bream       DHA  2.730000
## 12       Herring       DHA  5.860000
## 13          Pike       DHA  0.300000
## 14 Rainbow trout       DHA  7.570000
## 15         Roach       DHA  2.870000
## 16        Salmon       DHA  6.690000
## 17       Vendace       DHA  3.000000
## 18     Whitefish       DHA  3.940000
## 19  Average fish      Fish  1.000000
## 20         Bream      Fish  1.000000
## 21       Herring      Fish  1.000000
## 22          Pike      Fish  1.000000
## 23 Rainbow trout      Fish  1.000000
## 24         Roach      Fish  1.000000
## 25        Salmon      Fish  1.000000
## 26       Vendace      Fish  1.000000
## 27     Whitefish      Fish  1.000000
## 28  Average fish    Omega3  7.000000
## 29         Bream    Omega3  6.000000
## 30       Herring    Omega3 24.000000
## 31          Pike    Omega3  0.500000
## 32 Rainbow trout    Omega3 18.000000
## 33         Roach    Omega3  5.000000
## 34        Salmon    Omega3 23.000000
## 35       Vendace    Omega3 10.000000
## 36     Whitefish    Omega3 10.000000
## 37         Bream      PFAS  4.950000
## 38           Eel      PFAS  7.895000
## 39       Herring      PFAS  1.655000
## 40         Perch      PFAS  8.793125
## 41    Pike-perch      PFAS  2.595000
## 42  Average fish Vitamin D  0.105000
## 43         Bream Vitamin D  0.140000
## 44       Herring Vitamin D  0.156000
## 45          Pike Vitamin D  0.021000
## 46 Rainbow trout Vitamin D  0.051000
## 47         Roach Vitamin D  0.100000
## 48        Salmon Vitamin D  0.067000
## 49       Vendace Vitamin D  0.094000
## 50     Whitefish Vitamin D  0.144000
oprint(summary(dose,"mean"))
##   Scaling       mean
## 1      BW 0.01428571
## 2   Log10 0.00000000
## 3    None 1.00000000
oprint(summary(ERF,"mean"))
##    Exposure_agent                                          Response
## 1            Fish                               All-cause mortality
## 2          Omega3                                     Breast cancer
## 3            Fish                                        Depression
## 4             DHA                         Loss in child's IQ points
## 5             TEQ                               Sperm concentration
## 6             TEQ                           Yes or no dental defect
## 7          Omega3                                    CHD2 mortality
## 8       Vitamin D                          Vitamin D recommendation
## 9            MeHg                         Loss in child's IQ points
## 10           PFAS                                 Immunosuppression
## 11           PFAS                                          PFAS TWI
## 12            TEQ                                  Cancer morbidity
## 13            TEQ      Dioxin recommendation tolerable daily intake
## 14            TEQ Dioxin recommendation tolerable daily intake 2018
## 15           Fish                               All-cause mortality
## 16         Omega3                                     Breast cancer
## 17           Fish                                        Depression
## 18            DHA                         Loss in child's IQ points
## 19            TEQ                               Sperm concentration
## 20            TEQ                           Yes or no dental defect
## 21         Omega3                                    CHD2 mortality
## 22      Vitamin D                          Vitamin D recommendation
## 23           MeHg                         Loss in child's IQ points
## 24           PFAS                                 Immunosuppression
## 25           PFAS                                          PFAS TWI
## 26            TEQ                                  Cancer morbidity
## 27            TEQ      Dioxin recommendation tolerable daily intake
## 28            TEQ Dioxin recommendation tolerable daily intake 2018
##      ER_function Scaling Observation          mean
## 1             RR    None         ERF   0.997871700
## 2             RR    None         ERF   0.999487200
## 3             RR    None         ERF   0.994690400
## 4            ERS    None         ERF  -0.001300000
## 5            ERS    None         ERF   0.000060000
## 6            ERS    None         ERF   0.001390971
## 7  Relative Hill    None         ERF  -0.170000000
## 8           Step    None         ERF 100.000000000
## 9            ERS      BW         ERF   9.800000000
## 10           ERS      BW         ERF   0.022700000
## 11           TWI      BW         ERF   4.400000000
## 12           CSF      BW         ERF   0.000500000
## 13           TDI      BW         ERF   2.000000000
## 14           TDI      BW         ERF   0.288900000
## 15            RR    None   Threshold   0.000000000
## 16            RR    None   Threshold   0.000000000
## 17            RR    None   Threshold   0.000000000
## 18           ERS    None   Threshold   0.000000000
## 19           ERS    None   Threshold   0.000000000
## 20           ERS    None   Threshold   0.000000000
## 21 Relative Hill    None   Threshold  47.000000000
## 22          Step    None   Threshold  10.000000000
## 23           ERS      BW   Threshold   0.000000000
## 24           ERS      BW   Threshold   0.000000000
## 25           TWI      BW   Threshold   0.000000000
## 26           CSF      BW   Threshold   0.000000000
## 27           TDI      BW   Threshold   0.000000000
## 28           TDI      BW   Threshold   0.000000000
oprint(summary(expo_dir,"mean"))
##              Fish  Compound            Kala Scenario              Age
## 1       Whitefish       ALA      Kasvatettu      BAU     Female 18-45
## 2       Whitefish       DHA      Kasvatettu      BAU     Female 18-45
## 3       Whitefish      Fish      Kasvatettu      BAU     Female 18-45
## 4       Whitefish    Omega3      Kasvatettu      BAU     Female 18-45
## 5       Whitefish Vitamin D      Kasvatettu      BAU     Female 18-45
## 6    Average fish       ALA     Kaupallinen      BAU     Female 18-45
## 7    Average fish       DHA     Kaupallinen      BAU     Female 18-45
## 8    Average fish      Fish     Kaupallinen      BAU     Female 18-45
## 9    Average fish    Omega3     Kaupallinen      BAU     Female 18-45
## 10   Average fish      PFAS     Kaupallinen      BAU     Female 18-45
## 11   Average fish Vitamin D     Kaupallinen      BAU     Female 18-45
## 12  Rainbow trout       ALA       Kirjolohi      BAU     Female 18-45
## 13  Rainbow trout       DHA       Kirjolohi      BAU     Female 18-45
## 14  Rainbow trout      Fish       Kirjolohi      BAU     Female 18-45
## 15  Rainbow trout    Omega3       Kirjolohi      BAU     Female 18-45
## 16  Rainbow trout Vitamin D       Kirjolohi      BAU     Female 18-45
## 17   Average fish       ALA      Muu tuonti      BAU     Female 18-45
## 18   Average fish       DHA      Muu tuonti      BAU     Female 18-45
## 19   Average fish      Fish      Muu tuonti      BAU     Female 18-45
## 20   Average fish    Omega3      Muu tuonti      BAU     Female 18-45
## 21   Average fish      PFAS      Muu tuonti      BAU     Female 18-45
## 22   Average fish Vitamin D      Muu tuonti      BAU     Female 18-45
## 23        Herring       ALA         Silakka      BAU     Female 18-45
## 24        Herring       DHA         Silakka      BAU     Female 18-45
## 25        Herring      Fish         Silakka      BAU     Female 18-45
## 26        Herring    Omega3         Silakka      BAU     Female 18-45
## 27        Herring      PFAS         Silakka      BAU     Female 18-45
## 28        Herring Vitamin D         Silakka      BAU     Female 18-45
## 29  Rainbow trout       ALA Tuontikirjolohi      BAU     Female 18-45
## 30  Rainbow trout       DHA Tuontikirjolohi      BAU     Female 18-45
## 31  Rainbow trout      Fish Tuontikirjolohi      BAU     Female 18-45
## 32  Rainbow trout    Omega3 Tuontikirjolohi      BAU     Female 18-45
## 33  Rainbow trout Vitamin D Tuontikirjolohi      BAU     Female 18-45
## 34         Salmon       ALA      Tuontilohi      BAU     Female 18-45
## 35         Salmon       DHA      Tuontilohi      BAU     Female 18-45
## 36         Salmon      Fish      Tuontilohi      BAU     Female 18-45
## 37         Salmon    Omega3      Tuontilohi      BAU     Female 18-45
## 38         Salmon Vitamin D      Tuontilohi      BAU     Female 18-45
## 39   Average fish       ALA      Vapaa-ajan      BAU     Female 18-45
## 40   Average fish       DHA      Vapaa-ajan      BAU     Female 18-45
## 41   Average fish      Fish      Vapaa-ajan      BAU     Female 18-45
## 42   Average fish    Omega3      Vapaa-ajan      BAU     Female 18-45
## 43   Average fish      PFAS      Vapaa-ajan      BAU     Female 18-45
## 44   Average fish Vitamin D      Vapaa-ajan      BAU     Female 18-45
## 45      Whitefish       ALA      Kasvatettu      BAU       Female 45+
## 46      Whitefish       DHA      Kasvatettu      BAU       Female 45+
## 47      Whitefish      Fish      Kasvatettu      BAU       Female 45+
## 48      Whitefish    Omega3      Kasvatettu      BAU       Female 45+
## 49      Whitefish Vitamin D      Kasvatettu      BAU       Female 45+
## 50   Average fish       ALA     Kaupallinen      BAU       Female 45+
## 51   Average fish       DHA     Kaupallinen      BAU       Female 45+
## 52   Average fish      Fish     Kaupallinen      BAU       Female 45+
## 53   Average fish    Omega3     Kaupallinen      BAU       Female 45+
## 54   Average fish      PFAS     Kaupallinen      BAU       Female 45+
## 55   Average fish Vitamin D     Kaupallinen      BAU       Female 45+
## 56  Rainbow trout       ALA       Kirjolohi      BAU       Female 45+
## 57  Rainbow trout       DHA       Kirjolohi      BAU       Female 45+
## 58  Rainbow trout      Fish       Kirjolohi      BAU       Female 45+
## 59  Rainbow trout    Omega3       Kirjolohi      BAU       Female 45+
## 60  Rainbow trout Vitamin D       Kirjolohi      BAU       Female 45+
## 61   Average fish       ALA      Muu tuonti      BAU       Female 45+
## 62   Average fish       DHA      Muu tuonti      BAU       Female 45+
## 63   Average fish      Fish      Muu tuonti      BAU       Female 45+
## 64   Average fish    Omega3      Muu tuonti      BAU       Female 45+
## 65   Average fish      PFAS      Muu tuonti      BAU       Female 45+
## 66   Average fish Vitamin D      Muu tuonti      BAU       Female 45+
## 67        Herring       ALA         Silakka      BAU       Female 45+
## 68        Herring       DHA         Silakka      BAU       Female 45+
## 69        Herring      Fish         Silakka      BAU       Female 45+
## 70        Herring    Omega3         Silakka      BAU       Female 45+
## 71        Herring      PFAS         Silakka      BAU       Female 45+
## 72        Herring Vitamin D         Silakka      BAU       Female 45+
## 73  Rainbow trout       ALA Tuontikirjolohi      BAU       Female 45+
## 74  Rainbow trout       DHA Tuontikirjolohi      BAU       Female 45+
## 75  Rainbow trout      Fish Tuontikirjolohi      BAU       Female 45+
## 76  Rainbow trout    Omega3 Tuontikirjolohi      BAU       Female 45+
## 77  Rainbow trout Vitamin D Tuontikirjolohi      BAU       Female 45+
## 78         Salmon       ALA      Tuontilohi      BAU       Female 45+
## 79         Salmon       DHA      Tuontilohi      BAU       Female 45+
## 80         Salmon      Fish      Tuontilohi      BAU       Female 45+
## 81         Salmon    Omega3      Tuontilohi      BAU       Female 45+
## 82         Salmon Vitamin D      Tuontilohi      BAU       Female 45+
## 83   Average fish       ALA      Vapaa-ajan      BAU       Female 45+
## 84   Average fish       DHA      Vapaa-ajan      BAU       Female 45+
## 85   Average fish      Fish      Vapaa-ajan      BAU       Female 45+
## 86   Average fish    Omega3      Vapaa-ajan      BAU       Female 45+
## 87   Average fish      PFAS      Vapaa-ajan      BAU       Female 45+
## 88   Average fish Vitamin D      Vapaa-ajan      BAU       Female 45+
## 89      Whitefish       ALA      Kasvatettu      BAU Non Female 18-45
## 90      Whitefish       DHA      Kasvatettu      BAU Non Female 18-45
## 91      Whitefish      Fish      Kasvatettu      BAU Non Female 18-45
## 92      Whitefish    Omega3      Kasvatettu      BAU Non Female 18-45
## 93      Whitefish Vitamin D      Kasvatettu      BAU Non Female 18-45
## 94   Average fish       ALA     Kaupallinen      BAU Non Female 18-45
## 95   Average fish       DHA     Kaupallinen      BAU Non Female 18-45
## 96   Average fish      Fish     Kaupallinen      BAU Non Female 18-45
## 97   Average fish    Omega3     Kaupallinen      BAU Non Female 18-45
## 98   Average fish      PFAS     Kaupallinen      BAU Non Female 18-45
## 99   Average fish Vitamin D     Kaupallinen      BAU Non Female 18-45
## 100 Rainbow trout       ALA       Kirjolohi      BAU Non Female 18-45
## 101 Rainbow trout       DHA       Kirjolohi      BAU Non Female 18-45
## 102 Rainbow trout      Fish       Kirjolohi      BAU Non Female 18-45
## 103 Rainbow trout    Omega3       Kirjolohi      BAU Non Female 18-45
## 104 Rainbow trout Vitamin D       Kirjolohi      BAU Non Female 18-45
## 105  Average fish       ALA      Muu tuonti      BAU Non Female 18-45
## 106  Average fish       DHA      Muu tuonti      BAU Non Female 18-45
## 107  Average fish      Fish      Muu tuonti      BAU Non Female 18-45
## 108  Average fish    Omega3      Muu tuonti      BAU Non Female 18-45
## 109  Average fish      PFAS      Muu tuonti      BAU Non Female 18-45
## 110  Average fish Vitamin D      Muu tuonti      BAU Non Female 18-45
## 111       Herring       ALA         Silakka      BAU Non Female 18-45
## 112       Herring       DHA         Silakka      BAU Non Female 18-45
## 113       Herring      Fish         Silakka      BAU Non Female 18-45
## 114       Herring    Omega3         Silakka      BAU Non Female 18-45
## 115       Herring      PFAS         Silakka      BAU Non Female 18-45
## 116       Herring Vitamin D         Silakka      BAU Non Female 18-45
## 117 Rainbow trout       ALA Tuontikirjolohi      BAU Non Female 18-45
## 118 Rainbow trout       DHA Tuontikirjolohi      BAU Non Female 18-45
## 119 Rainbow trout      Fish Tuontikirjolohi      BAU Non Female 18-45
## 120 Rainbow trout    Omega3 Tuontikirjolohi      BAU Non Female 18-45
## 121 Rainbow trout Vitamin D Tuontikirjolohi      BAU Non Female 18-45
## 122        Salmon       ALA      Tuontilohi      BAU Non Female 18-45
## 123        Salmon       DHA      Tuontilohi      BAU Non Female 18-45
## 124        Salmon      Fish      Tuontilohi      BAU Non Female 18-45
## 125        Salmon    Omega3      Tuontilohi      BAU Non Female 18-45
## 126        Salmon Vitamin D      Tuontilohi      BAU Non Female 18-45
## 127  Average fish       ALA      Vapaa-ajan      BAU Non Female 18-45
## 128  Average fish       DHA      Vapaa-ajan      BAU Non Female 18-45
## 129  Average fish      Fish      Vapaa-ajan      BAU Non Female 18-45
## 130  Average fish    Omega3      Vapaa-ajan      BAU Non Female 18-45
## 131  Average fish      PFAS      Vapaa-ajan      BAU Non Female 18-45
## 132  Average fish Vitamin D      Vapaa-ajan      BAU Non Female 18-45
## 133     Whitefish       ALA      Kasvatettu      BAU   Non Female 45+
## 134     Whitefish       DHA      Kasvatettu      BAU   Non Female 45+
## 135     Whitefish      Fish      Kasvatettu      BAU   Non Female 45+
## 136     Whitefish    Omega3      Kasvatettu      BAU   Non Female 45+
## 137     Whitefish Vitamin D      Kasvatettu      BAU   Non Female 45+
## 138  Average fish       ALA     Kaupallinen      BAU   Non Female 45+
## 139  Average fish       DHA     Kaupallinen      BAU   Non Female 45+
## 140  Average fish      Fish     Kaupallinen      BAU   Non Female 45+
## 141  Average fish    Omega3     Kaupallinen      BAU   Non Female 45+
## 142  Average fish      PFAS     Kaupallinen      BAU   Non Female 45+
## 143  Average fish Vitamin D     Kaupallinen      BAU   Non Female 45+
## 144 Rainbow trout       ALA       Kirjolohi      BAU   Non Female 45+
## 145 Rainbow trout       DHA       Kirjolohi      BAU   Non Female 45+
## 146 Rainbow trout      Fish       Kirjolohi      BAU   Non Female 45+
## 147 Rainbow trout    Omega3       Kirjolohi      BAU   Non Female 45+
## 148 Rainbow trout Vitamin D       Kirjolohi      BAU   Non Female 45+
## 149  Average fish       ALA      Muu tuonti      BAU   Non Female 45+
## 150  Average fish       DHA      Muu tuonti      BAU   Non Female 45+
## 151  Average fish      Fish      Muu tuonti      BAU   Non Female 45+
## 152  Average fish    Omega3      Muu tuonti      BAU   Non Female 45+
## 153  Average fish      PFAS      Muu tuonti      BAU   Non Female 45+
## 154  Average fish Vitamin D      Muu tuonti      BAU   Non Female 45+
## 155       Herring       ALA         Silakka      BAU   Non Female 45+
## 156       Herring       DHA         Silakka      BAU   Non Female 45+
## 157       Herring      Fish         Silakka      BAU   Non Female 45+
## 158       Herring    Omega3         Silakka      BAU   Non Female 45+
## 159       Herring      PFAS         Silakka      BAU   Non Female 45+
## 160       Herring Vitamin D         Silakka      BAU   Non Female 45+
## 161 Rainbow trout       ALA Tuontikirjolohi      BAU   Non Female 45+
## 162 Rainbow trout       DHA Tuontikirjolohi      BAU   Non Female 45+
## 163 Rainbow trout      Fish Tuontikirjolohi      BAU   Non Female 45+
## 164 Rainbow trout    Omega3 Tuontikirjolohi      BAU   Non Female 45+
## 165 Rainbow trout Vitamin D Tuontikirjolohi      BAU   Non Female 45+
## 166        Salmon       ALA      Tuontilohi      BAU   Non Female 45+
## 167        Salmon       DHA      Tuontilohi      BAU   Non Female 45+
## 168        Salmon      Fish      Tuontilohi      BAU   Non Female 45+
## 169        Salmon    Omega3      Tuontilohi      BAU   Non Female 45+
## 170        Salmon Vitamin D      Tuontilohi      BAU   Non Female 45+
## 171  Average fish       ALA      Vapaa-ajan      BAU   Non Female 45+
## 172  Average fish       DHA      Vapaa-ajan      BAU   Non Female 45+
## 173  Average fish      Fish      Vapaa-ajan      BAU   Non Female 45+
## 174  Average fish    Omega3      Vapaa-ajan      BAU   Non Female 45+
## 175  Average fish      PFAS      Vapaa-ajan      BAU   Non Female 45+
## 176  Average fish Vitamin D      Vapaa-ajan      BAU   Non Female 45+
##             mean
## 1     0.29916466
## 2     0.53094990
## 3     0.13475886
## 4     1.34758857
## 5     0.01940528
## 6     0.49591260
## 7     1.82553332
## 8     0.71871391
## 9     5.03099734
## 10    4.61968337
## 11    0.07546496
## 12    7.23812283
## 13   11.39139082
## 14    1.50480724
## 15   27.08653035
## 16    0.07674517
## 17    4.33923521
## 18   15.97341657
## 19    6.28874668
## 20   44.02122677
## 21   40.42222945
## 22    0.66031840
## 23    0.62528110
## 24    2.10583175
## 25    0.35935695
## 26    8.62456688
## 27    0.59473576
## 28    0.05605968
## 29    5.18552083
## 30    8.16099641
## 31    1.07807086
## 32   19.40527547
## 33    0.05498161
## 34   42.19209987
## 35   35.46044575
## 36    5.30051506
## 37  121.91184637
## 38    0.35513451
## 39    1.53422959
## 40    5.64774372
## 41    2.22352115
## 42   15.56464804
## 43   14.29214541
## 44    0.23346972
## 45    0.74791166
## 46    1.32737475
## 47    0.33689714
## 48    3.36897144
## 49    0.04851319
## 50    1.23978149
## 51    4.56383331
## 52    1.79678477
## 53   12.57749336
## 54   11.54920841
## 55    0.18866240
## 56   18.09530708
## 57   28.47847705
## 58    3.76201810
## 59   67.71632587
## 60    0.19186292
## 61   10.84808802
## 62   39.93354142
## 63   15.72186670
## 64  110.05306692
## 65  101.05557362
## 66    1.65079600
## 67    1.56320275
## 68    5.26457936
## 69    0.89839238
## 70   21.56141719
## 71    1.48683939
## 72    0.14014921
## 73   12.96380209
## 74   20.40249102
## 75    2.69517715
## 76   48.51318868
## 77    0.13745403
## 78  105.48024969
## 79   88.65111437
## 80   13.25128765
## 81  304.77961593
## 82    0.88783627
## 83    3.83557398
## 84   14.11935929
## 85    5.55880287
## 86   38.91162009
## 87   35.73036353
## 88    0.58367430
## 89    0.59832933
## 90    1.06189980
## 91    0.26951771
## 92    2.69517715
## 93    0.03881055
## 94    0.99182519
## 95    3.65106664
## 96    1.43742781
## 97   10.06199469
## 98    9.23936673
## 99    0.15092992
## 100  14.47624566
## 101  22.78278164
## 102   3.00961448
## 103  54.17306069
## 104   0.15349034
## 105   8.67847042
## 106  31.94683314
## 107  12.57749336
## 108  88.04245353
## 109  80.84445889
## 110   1.32063680
## 111   1.25056220
## 112   4.21166349
## 113   0.71871391
## 114  17.24913375
## 115   1.18947152
## 116   0.11211937
## 117  10.37104167
## 118  16.32199281
## 119   2.15614172
## 120  38.81055094
## 121   0.10996323
## 122  84.38419975
## 123  70.92089150
## 124  10.60103012
## 125 243.82369274
## 126   0.71026902
## 127   3.06845918
## 128  11.29548743
## 129   4.44704230
## 130  31.12929607
## 131  28.58429082
## 132   0.46693944
## 133   0.99721555
## 134   1.76983299
## 135   0.44919619
## 136   4.49196191
## 137   0.06468425
## 138   1.65304198
## 139   6.08511107
## 140   2.39571302
## 141  16.76999115
## 142  15.39894455
## 143   0.25154987
## 144  24.12707611
## 145  37.97130273
## 146   5.01602414
## 147  90.28843449
## 148   0.25581723
## 149  14.46411737
## 150  53.24472190
## 151  20.96248894
## 152 146.73742255
## 153 134.74076482
## 154   2.20106134
## 155   2.08427033
## 156   7.01943915
## 157   1.19785651
## 158  28.74855626
## 159   1.98245253
## 160   0.18686562
## 161  17.28506945
## 162  27.20332136
## 163   3.59356953
## 164  64.68425157
## 165   0.18327205
## 166 140.64033291
## 167 118.20148583
## 168  17.66838353
## 169 406.37282123
## 170   1.18378170
## 171   5.11409864
## 172  18.82581239
## 173   7.41173716
## 174  51.88216012
## 175  47.64048471
## 176   0.77823240
#oprint(summary(exposure,"mean"))
oprint(summary(fish_proportion,"mean"))
##                Age      mean
## 1     Female 18-45 0.4528302
## 2       Female 45+ 1.1320755
## 3 Non Female 18-45 0.9056604
## 4   Non Female 45+ 1.5094340
oprint(summary(incidence,"mean"))
##                                            Response       Age Adjust   mean
## 1                         Loss in child's IQ points     0 - 4    BAU 1.1920
## 2                               Sperm concentration     0 - 4    BAU 0.0140
## 3                           Yes or no dental defect     0 - 4    BAU 0.0448
## 4      Dioxin recommendation tolerable daily intake Undefined    BAU 0.1100
## 5 Dioxin recommendation tolerable daily intake 2018 Undefined    BAU 0.3200
## 6                                          PFAS TWI Undefined    BAU 1.0000
## 7                          Vitamin D recommendation Undefined    BAU 0.2200
oprint(summary(PAF,"mean"))
##    Exposure_agent                                          Response       Age
## 1             DHA                         Loss in child's IQ points     0 - 4
## 2            MeHg                         Loss in child's IQ points     0 - 4
## 3             TEQ                               Sperm concentration     0 - 4
## 4             TEQ                           Yes or no dental defect     0 - 4
## 5            Fish                               All-cause mortality     0 - 4
## 6          Omega3                                     Breast cancer     0 - 4
## 7            Fish                                        Depression     0 - 4
## 8          Omega3                                    CHD2 mortality     0 - 4
## 9            Fish                               All-cause mortality   10 - 14
## 10         Omega3                                     Breast cancer   10 - 14
## 11           Fish                                        Depression   10 - 14
## 12         Omega3                                    CHD2 mortality   10 - 14
## 13           Fish                               All-cause mortality   15 - 19
## 14         Omega3                                     Breast cancer   15 - 19
## 15           Fish                                        Depression   15 - 19
## 16         Omega3                                    CHD2 mortality   15 - 19
## 17           Fish                               All-cause mortality   20 - 24
## 18         Omega3                                     Breast cancer   20 - 24
## 19           Fish                                        Depression   20 - 24
## 20         Omega3                                    CHD2 mortality   20 - 24
## 21           Fish                               All-cause mortality   25 - 29
## 22         Omega3                                     Breast cancer   25 - 29
## 23           Fish                                        Depression   25 - 29
## 24         Omega3                                    CHD2 mortality   25 - 29
## 25           Fish                               All-cause mortality   30 - 34
## 26         Omega3                                     Breast cancer   30 - 34
## 27           Fish                                        Depression   30 - 34
## 28         Omega3                                    CHD2 mortality   30 - 34
## 29           Fish                               All-cause mortality   35 - 39
## 30         Omega3                                     Breast cancer   35 - 39
## 31           Fish                                        Depression   35 - 39
## 32         Omega3                                    CHD2 mortality   35 - 39
## 33           Fish                               All-cause mortality   40 - 44
## 34         Omega3                                     Breast cancer   40 - 44
## 35           Fish                                        Depression   40 - 44
## 36         Omega3                                    CHD2 mortality   40 - 44
## 37           Fish                               All-cause mortality   45 - 49
## 38         Omega3                                     Breast cancer   45 - 49
## 39           Fish                                        Depression   45 - 49
## 40         Omega3                                    CHD2 mortality   45 - 49
## 41           Fish                               All-cause mortality     5 - 9
## 42         Omega3                                     Breast cancer     5 - 9
## 43           Fish                                        Depression     5 - 9
## 44         Omega3                                    CHD2 mortality     5 - 9
## 45           Fish                               All-cause mortality   50 - 54
## 46         Omega3                                     Breast cancer   50 - 54
## 47           Fish                                        Depression   50 - 54
## 48         Omega3                                    CHD2 mortality   50 - 54
## 49           Fish                               All-cause mortality   55 - 59
## 50         Omega3                                     Breast cancer   55 - 59
## 51           Fish                                        Depression   55 - 59
## 52         Omega3                                    CHD2 mortality   55 - 59
## 53           Fish                               All-cause mortality   60 - 64
## 54         Omega3                                     Breast cancer   60 - 64
## 55           Fish                                        Depression   60 - 64
## 56         Omega3                                    CHD2 mortality   60 - 64
## 57           Fish                               All-cause mortality   65 - 69
## 58         Omega3                                     Breast cancer   65 - 69
## 59           Fish                                        Depression   65 - 69
## 60         Omega3                                    CHD2 mortality   65 - 69
## 61           Fish                               All-cause mortality   70 - 74
## 62         Omega3                                     Breast cancer   70 - 74
## 63           Fish                                        Depression   70 - 74
## 64         Omega3                                    CHD2 mortality   70 - 74
## 65           Fish                               All-cause mortality   75 - 79
## 66         Omega3                                     Breast cancer   75 - 79
## 67           Fish                                        Depression   75 - 79
## 68         Omega3                                    CHD2 mortality   75 - 79
## 69           Fish                               All-cause mortality   80 - 84
## 70         Omega3                                     Breast cancer   80 - 84
## 71           Fish                                        Depression   80 - 84
## 72         Omega3                                    CHD2 mortality   80 - 84
## 73           Fish                               All-cause mortality       85+
## 74         Omega3                                     Breast cancer       85+
## 75           Fish                                        Depression       85+
## 76         Omega3                                    CHD2 mortality       85+
## 77            TEQ      Dioxin recommendation tolerable daily intake Undefined
## 78            TEQ Dioxin recommendation tolerable daily intake 2018 Undefined
## 79      Vitamin D                          Vitamin D recommendation Undefined
## 80           PFAS                                          PFAS TWI Undefined
##    Adjust         mean
## 1     BAU -0.001090604
## 2     BAU  0.117449664
## 3     BAU  0.004285714
## 4     BAU  0.031048457
## 5     BAU -0.002128300
## 6     BAU -0.000512800
## 7     BAU -0.005309600
## 8     BAU -0.003541667
## 9     BAU -0.002128300
## 10    BAU -0.000512800
## 11    BAU -0.005309600
## 12    BAU -0.003541667
## 13    BAU -0.002128300
## 14    BAU -0.000512800
## 15    BAU -0.005309600
## 16    BAU -0.003541667
## 17    BAU -0.002128300
## 18    BAU -0.000512800
## 19    BAU -0.005309600
## 20    BAU -0.003541667
## 21    BAU -0.002128300
## 22    BAU -0.000512800
## 23    BAU -0.005309600
## 24    BAU -0.003541667
## 25    BAU -0.002128300
## 26    BAU -0.000512800
## 27    BAU -0.005309600
## 28    BAU -0.003541667
## 29    BAU -0.002128300
## 30    BAU -0.000512800
## 31    BAU -0.005309600
## 32    BAU -0.003541667
## 33    BAU -0.002128300
## 34    BAU -0.000512800
## 35    BAU -0.005309600
## 36    BAU -0.003541667
## 37    BAU -0.002128300
## 38    BAU -0.000512800
## 39    BAU -0.005309600
## 40    BAU -0.003541667
## 41    BAU -0.002128300
## 42    BAU -0.000512800
## 43    BAU -0.005309600
## 44    BAU -0.003541667
## 45    BAU -0.002128300
## 46    BAU -0.000512800
## 47    BAU -0.005309600
## 48    BAU -0.003541667
## 49    BAU -0.002128300
## 50    BAU -0.000512800
## 51    BAU -0.005309600
## 52    BAU -0.003541667
## 53    BAU -0.002128300
## 54    BAU -0.000512800
## 55    BAU -0.005309600
## 56    BAU -0.003541667
## 57    BAU -0.002128300
## 58    BAU -0.000512800
## 59    BAU -0.005309600
## 60    BAU -0.003541667
## 61    BAU -0.002128300
## 62    BAU -0.000512800
## 63    BAU -0.005309600
## 64    BAU -0.003541667
## 65    BAU -0.002128300
## 66    BAU -0.000512800
## 67    BAU -0.005309600
## 68    BAU -0.003541667
## 69    BAU -0.002128300
## 70    BAU -0.000512800
## 71    BAU -0.005309600
## 72    BAU -0.003541667
## 73    BAU -0.002128300
## 74    BAU -0.000512800
## 75    BAU -0.005309600
## 76    BAU -0.003541667
## 77    BAU -8.090909091
## 78    BAU -2.125000000
## 79    BAU  1.000000000
## 80    BAU  0.000000000
oprint(summary(population,"mean"))
##    Gender       Age    mean
## 1  Female     0 - 4  125040
## 2    Male     0 - 4  130884
## 3  Female   10 - 14  151113
## 4    Male   10 - 14  157712
## 5  Female   15 - 19  144441
## 6    Male   15 - 19  152230
## 7  Female   20 - 24  152265
## 8    Male   20 - 24  161679
## 9  Female   25 - 29  172593
## 10   Male   25 - 29  183092
## 11 Female   30 - 34  169653
## 12   Male   30 - 34  181115
## 13 Female   35 - 39  174660
## 14   Male   35 - 39  186122
## 15 Female   40 - 44  168547
## 16   Male   40 - 44  177928
## 17 Female   45 - 49  154391
## 18   Male   45 - 49  159982
## 19 Female     5 - 9  149633
## 20   Male     5 - 9  156654
## 21 Female   50 - 54  176612
## 22   Male   50 - 54  179182
## 23 Female   55 - 59  185152
## 24   Male   55 - 59  183719
## 25 Female   60 - 64  183336
## 26   Male   60 - 64  176283
## 27 Female   65 - 69  185685
## 28   Male   65 - 69  171275
## 29 Female   70 - 74  186034
## 30   Male   70 - 74  163697
## 31 Female   75 - 79  118190
## 32   Male   75 - 79   93987
## 33 Female   80 - 84   96256
## 34   Male   80 - 84   65140
## 35 Female       85+  103429
## 36   Male       85+   47581
## 37 Female Undefined 2797030
## 38   Male Undefined 2728262
oprint(summary(RR,"mean"))
##    Exposure_agent            Response   ER_function Scaling     Age      mean
## 1            Fish All-cause mortality            RR    None   0 - 4 0.9978717
## 2          Omega3       Breast cancer            RR    None   0 - 4 0.9994872
## 3            Fish          Depression            RR    None   0 - 4 0.9946904
## 4          Omega3      CHD2 mortality Relative Hill    None   0 - 4 0.9964583
## 5            Fish All-cause mortality            RR    None 10 - 14 0.9978717
## 6          Omega3       Breast cancer            RR    None 10 - 14 0.9994872
## 7            Fish          Depression            RR    None 10 - 14 0.9946904
## 8          Omega3      CHD2 mortality Relative Hill    None 10 - 14 0.9964583
## 9            Fish All-cause mortality            RR    None 15 - 19 0.9978717
## 10         Omega3       Breast cancer            RR    None 15 - 19 0.9994872
## 11           Fish          Depression            RR    None 15 - 19 0.9946904
## 12         Omega3      CHD2 mortality Relative Hill    None 15 - 19 0.9964583
## 13           Fish All-cause mortality            RR    None 20 - 24 0.9978717
## 14         Omega3       Breast cancer            RR    None 20 - 24 0.9994872
## 15           Fish          Depression            RR    None 20 - 24 0.9946904
## 16         Omega3      CHD2 mortality Relative Hill    None 20 - 24 0.9964583
## 17           Fish All-cause mortality            RR    None 25 - 29 0.9978717
## 18         Omega3       Breast cancer            RR    None 25 - 29 0.9994872
## 19           Fish          Depression            RR    None 25 - 29 0.9946904
## 20         Omega3      CHD2 mortality Relative Hill    None 25 - 29 0.9964583
## 21           Fish All-cause mortality            RR    None 30 - 34 0.9978717
## 22         Omega3       Breast cancer            RR    None 30 - 34 0.9994872
## 23           Fish          Depression            RR    None 30 - 34 0.9946904
## 24         Omega3      CHD2 mortality Relative Hill    None 30 - 34 0.9964583
## 25           Fish All-cause mortality            RR    None 35 - 39 0.9978717
## 26         Omega3       Breast cancer            RR    None 35 - 39 0.9994872
## 27           Fish          Depression            RR    None 35 - 39 0.9946904
## 28         Omega3      CHD2 mortality Relative Hill    None 35 - 39 0.9964583
## 29           Fish All-cause mortality            RR    None 40 - 44 0.9978717
## 30         Omega3       Breast cancer            RR    None 40 - 44 0.9994872
## 31           Fish          Depression            RR    None 40 - 44 0.9946904
## 32         Omega3      CHD2 mortality Relative Hill    None 40 - 44 0.9964583
## 33           Fish All-cause mortality            RR    None 45 - 49 0.9978717
## 34         Omega3       Breast cancer            RR    None 45 - 49 0.9994872
## 35           Fish          Depression            RR    None 45 - 49 0.9946904
## 36         Omega3      CHD2 mortality Relative Hill    None 45 - 49 0.9964583
## 37           Fish All-cause mortality            RR    None   5 - 9 0.9978717
## 38         Omega3       Breast cancer            RR    None   5 - 9 0.9994872
## 39           Fish          Depression            RR    None   5 - 9 0.9946904
## 40         Omega3      CHD2 mortality Relative Hill    None   5 - 9 0.9964583
## 41           Fish All-cause mortality            RR    None 50 - 54 0.9978717
## 42         Omega3       Breast cancer            RR    None 50 - 54 0.9994872
## 43           Fish          Depression            RR    None 50 - 54 0.9946904
## 44         Omega3      CHD2 mortality Relative Hill    None 50 - 54 0.9964583
## 45           Fish All-cause mortality            RR    None 55 - 59 0.9978717
## 46         Omega3       Breast cancer            RR    None 55 - 59 0.9994872
## 47           Fish          Depression            RR    None 55 - 59 0.9946904
## 48         Omega3      CHD2 mortality Relative Hill    None 55 - 59 0.9964583
## 49           Fish All-cause mortality            RR    None 60 - 64 0.9978717
## 50         Omega3       Breast cancer            RR    None 60 - 64 0.9994872
## 51           Fish          Depression            RR    None 60 - 64 0.9946904
## 52         Omega3      CHD2 mortality Relative Hill    None 60 - 64 0.9964583
## 53           Fish All-cause mortality            RR    None 65 - 69 0.9978717
## 54         Omega3       Breast cancer            RR    None 65 - 69 0.9994872
## 55           Fish          Depression            RR    None 65 - 69 0.9946904
## 56         Omega3      CHD2 mortality Relative Hill    None 65 - 69 0.9964583
## 57           Fish All-cause mortality            RR    None 70 - 74 0.9978717
## 58         Omega3       Breast cancer            RR    None 70 - 74 0.9994872
## 59           Fish          Depression            RR    None 70 - 74 0.9946904
## 60         Omega3      CHD2 mortality Relative Hill    None 70 - 74 0.9964583
## 61           Fish All-cause mortality            RR    None 75 - 79 0.9978717
## 62         Omega3       Breast cancer            RR    None 75 - 79 0.9994872
## 63           Fish          Depression            RR    None 75 - 79 0.9946904
## 64         Omega3      CHD2 mortality Relative Hill    None 75 - 79 0.9964583
## 65           Fish All-cause mortality            RR    None 80 - 84 0.9978717
## 66         Omega3       Breast cancer            RR    None 80 - 84 0.9994872
## 67           Fish          Depression            RR    None 80 - 84 0.9946904
## 68         Omega3      CHD2 mortality Relative Hill    None 80 - 84 0.9964583
## 69           Fish All-cause mortality            RR    None     85+ 0.9978717
## 70         Omega3       Breast cancer            RR    None     85+ 0.9994872
## 71           Fish          Depression            RR    None     85+ 0.9946904
## 72         Omega3      CHD2 mortality Relative Hill    None     85+ 0.9964583
###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)

plot_ly(trim(amount), x=~Scenario, y=~amountResult, color=~Kala, type="bar") %>%
  layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
  layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
tmp <- exposure / Ovariable(
  output = data.frame(
    Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
    Result = c(1, 1, 1000, 1000, 1000, 1, 1)
  ),
  marginal = c(TRUE, FALSE)
)

plot_ly(trim(tmp), x=~exposureSource, y=~Result, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
  layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))
cat("Kalaperäisiä tautitaakkoja Suomessa\n")
## Kalaperäisiä tautitaakkoja Suomessa
if(openv$N>1) {
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
  tmp <- data.frame(
    Altiste = tmp$Exposure_agent,
    Vaikutus = tmp$Response,
    Keskiarvo = as.character(signif(tmp$mean,2)),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )#[rev(match(lev, tmp$Exposure_agent)),]

  oprint(tmp)
  
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
  tmp <- data.frame(
    Terveysvaikutus = tmp$Response,
    Keskiarvo = signif(tmp$mean,2),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )
  oprint(tmp)
}

ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()

ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()

plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
  layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
## Loading objects:
##   makeGraph
gr <- makeGraph(gr)
## Loading required package: DiagrammeR
## Loading objects:
##   formatted
## Loading objects:
##   chooseGr
#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")
## Loading objects:
##   showind
##   binoptest
##   showLoctable
##   ovashapetest
showLoctable()
showind()
## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.